Tutorial 1: Adaptation Techniques for Statistical Speech Recognition (Part I-III)
Adaptation is a technique to make better use of existing models for test data from new acoustic or linguistic conditions. It is an important and challenging research area of statistical speech recognition. This tutorial gives a systematic review of fundamental theories as well as introduction of state-of-the-art adaptation techniques. It includes both acoustic and language model adaptation. Following a simple example of acoustic model adaptation, basic concepts, procedures and categories of adaptation will be introduced. Then, a number of advanced adaptation techniques will be discussed, such as discriminative adaptation, Deep Neural Network adaptation, adaptive training, relationship to noise robustness etc. After the detailed review of acoustic model adaptation, an introduction of language model adaptation, such as topic adaptation will also be given. The whole tutorial is then summarised and future research direction will be discussed.